Amazon Kinesis vs. Azure HDInsight

Amazon Kinesis is a great replacement for Kafka and it works better whenever the components of the solution are AWS based. Best if extended fan-out is not required, but still price-performance ratio is very good for simplifying maintenance.

I would go with a different option if the systems to be connected are legacy, for instance in the case of traditional messaging clients.

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Well suited: A tiny-mid sized company with no immediate plans of growing the volume of their data processing, that can afford long response times from support. Also it helps if you are not prone to put your hands on Linux and Spark configuration. In fact, it can make things go really faster if you also work with the bundle-in Jupyter. And, if you need to perform some diagnostics and / or administrative tasks, that's full of tools to find an understand the Root Cause. Ideal for non experts. Less appropriate: Big Data company, intense on demand cluster creation, mission critical, costs reduction, latest versions of libraries required, sophisticate customizations required.

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• Processing huge loads of data
• Integrating well with IoT Platform on Amazon
• Integration with overall AWS Ecosystem
• Scalability

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• Data is presented without interfering others (IT or other dept).
• Data is managed properly and is available for retrievable any time.
• Legacy use of CD/DVD and Pendrive are not required.

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• Not a queue system, so little visibility into "backlog" if there is any
• Confusing terminology to make sure events aren't missed
• Sometimes didn't seem to trigger Lambda functions, or dropped events when a lot came in

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• The only problem I have come across is when loading large volumes of data I sometimes get an error message, I assume this means something is corrupt from within. I would love a way for this to be resolved without having to start over.

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Azure HDInsight is usable on the top of Azure Data Lake and gives us the benefit of analyzing large scale data workload in Hadoop. Usability and support from Microsoft are outstanding.

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The documentation was confusing and lacked examples. The streams suddenly stopped working with no explanation and there was no information in the logs. All these were more difficult when dealing with enhanced fan-out. In fact, we were about to abort the usage of Kinesis due to a misunderstanding with enhanced fan-out.

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Inexpert, isolated teams... not good for support an excessively complex platform. Lots of weeks or months for a complex problem troubleshoot. Many time lost stuck on MindTree, before the case was finally escalated with Microsoft!

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The main benefit was around set up - incredibly easy to just start using Kinesis. Kinesis is a real-time data processing platform, while Kafka is more of a message queue system. If you only need a message queue from a limited source, Kafka may do the job. More complex use cases, with low latency, higher volume of data, real time decisions and integration with multiple sources and destination at a decent price, Kinesis is better.

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At this time I have not used any other similar products... I am open to it but Azure HDInsight and its components really work well for our organization.

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• Caused us to need to re-engineer some basic re-try logic
• Caused us to drop some content without knowing it
• Made monitoring much more difficult
• We eventually switched back to SQS because Kinesis is not the same as a Queue system

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• ROI is of course there, as no legacy software for data presentation.
• No manual intervention for data retrieval.
• Data is available anywhere as requested.

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